A Componential Coding Neural Network Based Signal Modelling for Condition Monitoring

Khalid Rabeyee, Yuandong Xu, Aisha Alashter, Fengshou Gu, Andrew D. Ball

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many condition monitoring (CM) techniques have been investigated for early fault detection and diagnosis in order to avoid unexpected breakdowns due to machinery failures. However, manual techniques require well-skilled labours which will increase the cost of the monitoring process and may not always be available at the site. One of the most promising approaches is to automate the monitoring process using artificial intelligence (AI) techniques. However, the majority of AI-based techniques have been developed in CM for the post-processing stage, whereas the critical tasks including feature extraction and selection are still manually processed. This study focuses on the extending AI techniques in all phases of CM process by using a Componential Coding Neural Network (CCNN) which has been found to have unique properties of being trained through unsupervised learning, capable of dealing with raw data sets, translation invariance and high computational efficiency. These advantages of CCNN make it particularly suitable for automated analysis of the vibration data arisen from typical machine components such as the rolling element bearings which exhibit periodic phenomena with high non-stationarity and strong noise contamination. The CCNN was evaluated using both simulated and experimental data collected from a healthy and two defective tapered roller bearings under different operating conditions. Both of the results showed the capability of CCNN in detecting the initial anomalies of roller element bearings.

Original languageEnglish
Title of host publicationAdvances in Asset Management and Condition Monitoring, COMADEM 2019
EditorsAndrew Ball, Len Gelman, B.K.N. Rao
PublisherSpringer, Cham
Pages559-572
Number of pages14
Volume166
ISBN (Electronic)9783030577452
ISBN (Print)9783030577445
DOIs
Publication statusPublished - 28 Aug 2020
Event32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference - University of Huddersfield, Huddersfield, United Kingdom
Duration: 3 Sep 20195 Sep 2019
Conference number: 32
http://www.comadem2019.com/ (Link to Conference Website)

Publication series

NameSmart Innovation, Systems and Technologies
PublisherSpringer
Volume166
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference
Abbreviated titleCOMADEM 2019
CountryUnited Kingdom
CityHuddersfield
Period3/09/195/09/19
Internet address

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